A case for decision science research in energy

A sustainable low carbon future is seen by most to center around breakthroughs in technology and the associated economics. Most of the attention has been on carbon sequestration, biofuels, renewable sources of electricity and the like. A number of states and countries have instituted policies to make some of these happen. Many also see electrification of transportation as an avenue to zero emission vehicles and energy security of net oil importing nations. All of these cause people to make choices, in many cases requiring changes in behavior. Introducers of technology know that the barrier to wide scale adoption is particularly high when it involves substitution of something familiar. The science of why people make the decisions they do, especially those involving green alternatives, merits further investigation, if for no other reason than that it may guide product and process development into areas with higher success rates of adoption. It will undoubtedly be effective in informing on policy. An example is in the area of solar energy. If the primary driver for adoption is “seen as being green”, then hiding photo voltaic devices inside shingles would be counterproductive, as also the policy of many neighborhoods to disallow visible displays of solar panels on homes.

The International Energy Agency (IEA) has posited that for any reasonable 2050 targets for atmospheric carbon dioxide nearly 40% of the mitigation has to be from energy efficiency. Their most recent forecast calls for 57% of carbon mitigation by 2030 as being from energy efficiency (and interestingly only 10% from carbon sequestration). Undoubtedly this will in large measure be accomplished with engineering designs that provide the same utility for less energy. This has been the case with up to 90% reduction in standby power of household appliances through the simple expedient of low energy power supplies and modified circuitry. Since standby power constitutes 10% or so of all electricity usage in IEA countries, this is a huge gain. The Energy Star and similar efforts have produced further results, although some of these fall in a different bucket, that of the same utility at a somewhat greater price. In the case of compact fluorescent bulbs, the initial price is higher but the life cycle cost is lower. Now this begins to get into the realm of decision science because the consumer is required to understand and appreciate life cycle costing. We are firmly in it for cases where the costs are substantially higher, as in the case of hybrid vehicles. Electric cars will get squarely into the behavioral arena from the standpoint of range anxiety, which is roughly defined as the fear of running out of charge.

Electrification of transportation is an RTEC priority because we see it as the fastest route to energy security through making electricity fungible with oil. Furthermore, well to wheel efficiency of electric cars is about 45% better than that of conventional cars and the tail pipe emissions are zero, although the burden is shifted to the power producer, where it is more tractable. Consequently, enabling the public’s acceptance of electric cars is an RTEC priority.

Addressing range anxiety and other behaviors falls at least in part in the area of decision science. Some of it can be addressed with technology. For example, Nissan’s introduction of the Leaf later this year will be accompanied by features such as remote monitoring of the state of charge of the battery and driver notification, including identification of the nearest charging station. But in most instances, technical advances only take us so far. When smart electricity meters are installed in homes, there is high variability in the manner in which the data are used by the homeowner. Behavioral studies are needed to guide the programs to achieve the best results. Non price interventions that rely on behavioral proclivities, such as conformance to societal norms, can likely be used to advantage.

In their matrix of program thrusts, DOE’s newly formed unit ARPAe has a matrix element that intersects social science efforts with transportation. RTEC believes that this could be a fruitful area of pursuit for RTI/Duke/UNC collaboration. One possible project would combine conventional survey based approaches with behavioral economics ones in addressing the electric car range problem. At this time this is based on guesswork premised upon beliefs regarding consumer preferences when driving conventional cars. Statements such as “the consumer expects a range of 300 miles” are rife. A definitive study of driving distances in metropolitan areas that are initial target of electric vehicle entry could then be used to devise behavioral studies, the results of which could be expected to drive out interventions, both price based and not. To aid this, the original study would be broken out by age, income and other relevant demographics. Finally, the interventions themselves could be tested on a population.

The foregoing notwithstanding, RTEC believes that the greatest gains for society in the realm of sustainable energy are going to come from simply using less. Consequently, a major focus will be to encourage and assist members in devising social science based research with this goal in mind.